Application of Machine Learning Techniques to Classify and Identify Galaxy Merger Events in the Candels Field

Galaxy mergers are dynamic systems that offer us a glimpse into the evolution of the cosmos and the galaxies that constitute it. However, with the advent of large astronomical surveys, it is becoming increasingly difficult to rely on humans to classify the vast number of astronomical images collected every year and find the images that capture these systems. In recent years, researchers have increasingly relied on machine learning and computer vision classifiers, and while these techniques have proven useful for classifying broad galaxy morphologies, they have struggled to identify galaxy mergers. A random forest classifier was applied to a subset of galaxies from the Cosmic Assembly Near-infrared Extragalactic Legacy Survey (CANDELS) to classify merger and non-merger events. 283 merging and 283 non-merging galaxies were selected from the five CANDELS fields, totaling a combined 566 galaxies for training and validation. The classifier was trained on a set of parameters measured for each galaxy, including mass, star formation rate, galactic half-light radius, as well as Concentration and Asymmetry measurements. The classifier performed with a mean accuracy of 92.31% and a precision of 0.9332 on the validation dataset. Additionally, a computer vision convolutional neural network was trained to analyze and classify images of merger and non-merger events in the same fields. Due to the small number of merger events present in the CANDELS fields, data augmentation was utilized to increase the dataset significantly and boost performance. The computer vision classifier performed with an accuracy of 87.87% and a precision of 0.8683 on validation data. The pre-trained convolutional neural network was then used to predicted classes for a dataset containing active galactic nuclei (AGN) hosting galaxies and a control sample, although no correlation was found between predicted classes and whether the galaxy hosts an AGN

Large High Resolution Displays for Co-Located Collaborative Intelligence Analysis

Large, high-resolution vertical displays carry the potential to increase the accuracy of collaborative sensemaking, given correctly designed visual analytics tools. From an exploratory user study using a fictional intelligence analysis task, we investigated how users interact with the display to construct spatial schemas and externalize information, as well as how they establish shared and private territories. We investigated the spatial strategies of users partitioned by tool type used (document- or entity-centric). We classified the types of territorial behavior exhibited in terms of how the users interacted with the display (integrated or independent workspaces). Next, we examined how territorial behavior impacted the common ground between the pairs of users. Finally, we recommend design guidelines for building co-located collaborative visual analytics tools specifically for use on large, high-resolution vertical displays

Good is generally more alike than bad in the information ecology: Investigating the case of (the ABC model of) group stereotypes

On most if not all evaluatively relevant dimensions such as the temperature level, taste intensity, and nutritional value of a meal, one range of adequate, positive states is framed by two ranges of inadequate, negative states, namely too much and too little. This distribution of positive and negative states in the information ecology results in a higher similarity of positive objects, people, and events to other positive stimuli as compared to the similarity of negative stimuli to other negative stimuli. In other words, there are fewer ways in which an object, a person, or an event can be positive as compared to negative. Oftentimes, there is only one way in which a stimulus can be positive (e.g., a good meal has to have an adequate temperature level, taste intensity, and nutritional value). In contrast, there are many different ways in which a stimulus can be negative (e.g., a bad meal can be too hot or too cold, too spicy or too bland, or too fat or too lean). This higher similarity of positive as compared to negative stimuli is important, as similarity greatly impacts speed and accuracy on virtually all levels of information processing, including attention, classification, categorization, judgment and decision making, and recognition and recall memory. Thus, if the difference in similarity between positive and negative stimuli is a general phenomenon, it predicts and may explain a variety of valence asymmetries in cognitive processing (e.g., positive as compared to negative stimuli are processed faster but less accurately). In my dissertation, I show that the similarity asymmetry is indeed a general phenomenon that is observed in thousands of words and pictures. Further, I show that the similarity asymmetry applies to social groups. Groups stereotyped as average on the two dimensions agency / socio-economic success (A) and conservative-progressive beliefs (B) are stereotyped as positive or high on communion (C), while groups stereotyped as extreme on A and B (e.g., managers, homeless people, punks, and religious people) are stereotyped as negative or low on C. As average groups are more similar to one another than extreme groups, according to this ABC model of group stereotypes, positive groups are mentally represented as more similar to one another than negative groups. Finally, I discuss implications of the ABC model of group stereotypes, pointing to avenues for future research on how stereotype content shapes social perception, cognition, and behavior

High-SNR Asymptotics of Mutual Information for Discrete Constellations

The high-signal-to-noise ratio (SNR) asymptotic behavior of the mutual information (MI) for discrete constellations over the scalar additive white Gaussian noise channel is studied. Exact asymptotic expressions for the MI for arbitrary one-dimensional constellations and input distributions are presented in the limit as the SNR tends to infinity. Using the relationship between the MI and the minimum mean-square error (MMSE), asymptotics of the MMSE are also developed. It is shown that for any input distribution, the MI and the MMSE have an asymptotic behavior proportional to a Gaussian Q-function, whose argument depends only on the minimum Euclidean distance of the constellation and the SNR. Closed-form expressions for the coefficients of these Q-functions are calculated

High-SNR Asymptotics of Mutual Information for Discrete Constellations with Applications to BICM

Asymptotic expressions of the mutual information between any discrete input and the corresponding output of the scalar additive white Gaussian noise channel are presented in the limit as the signal-to-noise ratio (SNR) tends to infinity. Asymptotic expressions of the symbol-error probability (SEP) and the minimum mean-square error (MMSE) achieved by estimating the channel input given the channel output are also developed. It is shown that for any input distribution, the conditional entropy of the channel input given the output, MMSE, and SEP have an asymptotic behavior proportional to the Gaussian Q-function. The argument of the Q-function depends only on the minimum Euclidean distance (MED) of the constellation and the SNR, and the proportionality constants are functions of the MED and the probabilities of the pairs of constellation points at MED. The developed expressions are then generalized to study the high-SNR behavior of the generalized mutual information (GMI) for bit-interleaved coded modulation (BICM). By means of these asymptotic expressions, the long-standing conjecture that Gray codes are the binary labelings that maximize the BICM-GMI at high SNR is proven. It is further shown that for any equally spaced constellation whose size is a power of two, there always exists an anti-Gray code giving the lowest BICM-GMI at high SNR.Research supported by the European Community’s Seventh’s Framework Programme (FP7/2007-2013) under grant agreements No. 271986 and No. 333680, by the Swedish Research Council, Sweden (under grants #621-2006-4872 and #621-2011-5950) and by the Ministerio de Economía y Competitividad of Spain (TEC2009-14504-C02-01, CSD2008-00010, and TEC2012-38800-C03-01)

Folk intuitions about reference change and the causal theory of reference

In this paper, we present and discuss the findings of two experiments about reference change. Cases of reference change have sometimes been invoked to challenge traditional versions of semantic externalism, but the relevant cases have never been tested empirically. The experiments we have conducted use variants of the famous Twin Earth scenario to test folk intuitions about whether natural kind terms such as ‘water’ or ‘salt’ switch reference after being constantly (mis)applied to different kinds. Our results indicate that this is indeed so. We argue that this finding is evidence against Saul Kripke’s causal-historical view of reference, and at least provisional evidence in favor of the causal source view of reference as suggested by Gareth Evans and Michael Devitt

Metrizability of Clifford topological semigroups

We prove that a topological Clifford semigroup $S$ is metrizable if and only if $S$ is an $M$-space and the set $E=\{e\in S:ee=e\}$ of idempotents of $S$ is a metrizable $G_\delta$-set in $S$. The same metrization criterion holds also for any countably compact Clifford topological semigroup $S$.Comment: 4 page